AWS Machine Learning Blog

DECEMBER 1, 2023

Additionally, these tools provide a comprehensive solution for faster workflows, enabling the following: Faster data preparation – SageMaker Canvas has over 300 built-in transformations and the ability to use natural language that can accelerate data preparation and making data ready for model building.

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ODSC - Open Data Science

SEPTEMBER 25, 2023

Tapping into these schemas and pulling out machine learning-ready features can be nontrivial as one needs to know where the data entity of interest lives (e.g., customers), what its relations are, and how they’re connected, and then write SQL, python, or other to join and aggregate to a granularity of interest.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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ODSC - Open Data Science

MARCH 13, 2023

First, there’s a need for preparing the data, aka data engineering basics. Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.

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AWS Machine Learning Blog

JUNE 17, 2024

With SageMaker Processing jobs, you can use a simplified, managed experience to run data preprocessing or postprocessing and model evaluation workloads on the SageMaker platform. Twilio needed to implement an MLOps pipeline that queried data from PrestoDB. For more information on processing jobs, see Process data.

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Dataconomy

JULY 28, 2023

These tools offer a wide range of functionalities to handle complex data preparation tasks efficiently. The tool also employs AI capabilities for automatically providing attribute names and short descriptions for reports, making it easy to use and efficient for data preparation.

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ODSC - Open Data Science

FEBRUARY 5, 2025

The Evolving AI Development Lifecycle Despite the revolutionary capabilities of LLMs, the core development lifecycle established by traditional natural language processing remains essential: Plan, Prepare Data, Engineer Model, Evaluate, Deploy, Operate, and Monitor. Previously, consultants spent weeks manually querying data.

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AWS Machine Learning Blog

MARCH 10, 2023

Starting today, you can connect to Amazon EMR Hive as a big data query engine to bring in large datasets for ML. Aggregating and preparing large amounts of data is a critical part of ML workflow. Solution overview With SageMaker Studio setups, data professionals can quickly identify and connect to existing EMR clusters.

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phData

FEBRUARY 7, 2024

Snowpark is the set of libraries and runtimes in Snowflake that securely deploy and process non-SQL code, including Python, Java, and Scala. A DataFrame is like a query that must be evaluated to retrieve data. An action causes the DataFrame to be evaluated and sends the corresponding SQL statement to the server for execution.

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IBM Journey to AI blog

MARCH 14, 2024

With newfound support for open formats such as Parquet and Apache Iceberg, Netezza enables data engineers, data scientists and data analysts to share data and run complex workloads without duplicating or performing additional ETL.

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phData

JUNE 18, 2024

In this blog, we’ll explain why you should prepare your data before use in machine learning , how to clean and preprocess the data, and a few tips and tricks about data preparation. Why Prepare Data for Machine Learning Models? It may hurt it by adding in irrelevant, noisy data.

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phData

AUGUST 2, 2024

These procedures are designed to automate repetitive tasks, implement business logic, and perform complex data transformations , increasing the productivity and efficiency of data processing workflows. Snowflake stored procedures and dbt Hooks are essential to modern data engineering and analytics workflows.

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phData

DECEMBER 4, 2023

Copy Into When loading data into Snowflake, the very first and most important rule to follow is: do not load data with SQL inserts! Loading small amounts of data is cumbersome and costly: Each insert is slow — and time is credits. Knowing this, you want to have data prepared in a way to optimize your load.

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IBM Journey to AI blog

JULY 17, 2023

Within watsonx.ai, users can take advantage of open-source frameworks like PyTorch, TensorFlow and scikit-learn alongside IBM’s entire machine learning and data science toolkit and its ecosystem tools for code-based and visual data science capabilities.

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phData

FEBRUARY 24, 2023

Alteryx provides organizations with an opportunity to automate access to data, analytics , data science, and process automation all in one, end-to-end platform. Its capabilities can be split into the following topics: automating inputs & outputs, data preparation, data enrichment, and data science.

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phData

FEBRUARY 9, 2023

There are several reasons why your organization might consider migrating your data from Amazon Web Services (AWS) Redshift to the Snowflake Data Cloud. As an experienced data engineering consulting company, phData has helped with numerous migrations to Snowflake.

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Pickl AI

OCTOBER 17, 2024

Tools like Apache NiFi, Talend, and Informatica provide user-friendly interfaces for designing workflows, integrating diverse data sources, and executing ETL processes efficiently. Choosing the right tool based on the organisation’s specific needs, such as data volume and complexity, is vital for optimising ETL efficiency.

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Pickl AI

SEPTEMBER 5, 2024

Data Preparation: Cleaning, transforming, and preparing data for analysis and modelling. Collaborating with Teams: Working with data engineers, analysts, and stakeholders to ensure data solutions meet business needs.

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ODSC - Open Data Science

AUGUST 9, 2023

For a comprehensive understanding of the practical applications, including a detailed code walkthrough from data preparation to model deployment, please join us at the ODSC APAC conference 2023. Now, let’s give you a taste of what’s in store (the GitHub code repository can be found here ). if the recipe is a dessert, 0.0

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for data engineering and MLOps workflows.

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ODSC - Open Data Science

JANUARY 29, 2024

Data, Engineering, and Programming Skills Programming Despite the rise of no-code platforms and AI code assistance, programming skills are still essential for training and fine-tuning LLM models, scripting for data processing, and integrating models into applications. Kubernetes: A long-established tool for containerized apps.

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AWS Machine Learning Blog

JANUARY 26, 2024

Define strict data ingress and egress rules to help protect against manipulation and exfiltration using VPCs with AWS Network Firewall policies.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. Saurabh Gupta is a Principal Engineer at Zeta Global.

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AWS Machine Learning Blog

OCTOBER 28, 2024

This minimizes the complexity and overhead associated with moving data between cloud environments, enabling organizations to access and utilize their disparate data assets for ML projects. You can use SageMaker Canvas to build the initial data preparation routine and generate accurate predictions without writing code.

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Dataconomy

MARCH 19, 2025

Key disciplines involved in data science Understanding the core disciplines within data science provides a comprehensive perspective on the field’s multifaceted nature. Overview of core disciplines Data science encompasses several key disciplines including data engineering, data preparation, and predictive analytics.

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